cograph is a modern R package for network visualization with rich node styling, multiple layouts, and first-class support for Transition Network Analysis (TNA).
# Install from CRAN (when available)
install.packages("cograph")
# Development version from GitHub
devtools::install_github("sonsoleslp/cograph")The primary use case: visualize transition networks from the
tna package.
library(tna)
library(cograph)
# Build a TNA model from sequence data
fit <- tna(engagement)
# One-liner visualization
splot(fit)
library(cograph)
# Create a transition matrix
states <- c("Explore", "Plan", "Monitor", "Adapt", "Reflect")
mat <- matrix(
c(0.0, 0.4, 0.2, 0.1, 0.3,
0.3, 0.0, 0.3, 0.2, 0.2,
0.2, 0.3, 0.0, 0.3, 0.2,
0.1, 0.2, 0.4, 0.0, 0.3,
0.2, 0.2, 0.2, 0.4, 0.0),
nrow = 5, byrow = TRUE,
dimnames = list(states, states)
)
splot(mat)
par(mfrow = c(2, 2), mar = c(1, 1, 2, 1))
splot(mat, layout = "oval", title = "oval")
splot(mat, layout = "circle", title = "circle")
splot(mat, layout = "kk", title = "kk")
splot(mat, layout = "fr", title = "fr")
shapes <- c("circle", "square", "hexagon", "diamond", "triangle")
splot(mat,
node_shape = shapes,
node_fill = c("#E63946", "#457B9D", "#2A9D8F", "#E9C46A", "#F4A261"),
layout = "circle"
)
Donut nodes show proportional fill with optional polygon shapes.
fills <- c(0.9, 0.7, 0.5, 0.3, 0.8)
splot(mat,
donut_fill = fills,
donut_color = "steelblue",
donut_shape = c("circle", "hexagon", "square", "diamond", "triangle")
)
Pie chart nodes with per-node color palettes.
pie_vals <- list(
c(0.5, 0.3, 0.2),
c(0.4, 0.4, 0.2),
c(0.3, 0.3, 0.4),
c(0.6, 0.2, 0.2),
c(0.2, 0.5, 0.3)
)
pie_cols <- list(
c("#E63946", "#457B9D", "#2A9D8F"),
c("#264653", "#E9C46A", "#F4A261"),
c("#F72585", "#7209B7", "#3A0CA3"),
c("#003049", "#D62828", "#F77F00"),
c("#606C38", "#283618", "#DDA15E")
)
splot(mat,
node_shape = "pie",
pie_values = pie_vals,
pie_colors = pie_cols,
layout = "circle"
)
Combine outer donut ring with inner pie segments.
splot(mat,
donut_fill = fills,
donut_color = "steelblue",
pie_values = pie_vals,
pie_colors = c("#E41A1C", "#377EB8", "#4DAF4A")
)
Heterogeneous multi-group networks with bipartite or polygon layouts.
# 8-state network with 2 groups
states8 <- c("Explore", "Plan", "Monitor", "Adapt",
"Reflect", "Regulate", "Execute", "Review")
mat8 <- matrix(runif(64, 0, 0.3), 8, 8,
dimnames = list(states8, states8))
diag(mat8) <- 0
groups <- list(
Cognitive = c("Explore", "Plan", "Monitor", "Adapt"),
Metacognitive = c("Reflect", "Regulate", "Execute", "Review")
)
plot_htna(mat8, groups,
group_colors = c("#ffd89d", "#a68ba5"),
group_shapes = c("circle", "square")
)
Multi-cluster networks with summary edges between clusters.
clusters <- list(
Planning = c("Explore", "Plan"),
Action = c("Monitor", "Adapt"),
Meta = c("Reflect", "Regulate"),
Review = c("Execute", "Review")
)
plot_mtna(mat8, clusters,
shapes = c("circle", "square", "diamond", "triangle"),
summary_edges = TRUE
)
Multilevel 3D perspective networks with stacked layers.
layers <- list(
Macro = c("Explore", "Plan"),
Meso = c("Monitor", "Adapt", "Reflect"),
Micro = c("Regulate", "Execute", "Review")
)
plot_mlna(mat8, layers,
layout = "horizontal",
layer_spacing = 2.5,
between_edges = TRUE
)
Sophisticated visualization: hexagon nodes with precision donut + probability pie.
# Precision as donut fill (outer ring)
precision <- c(0.95, 0.87, 0.72, 0.91, 0.68)
# Probability distribution as pie (inner)
prob_vals <- list(
c(0.6, 0.3, 0.1),
c(0.5, 0.4, 0.1),
c(0.4, 0.3, 0.3),
c(0.7, 0.2, 0.1),
c(0.3, 0.4, 0.3)
)
splot(mat,
node_shape = "hexagon",
donut_fill = precision,
donut_color = "#2E7D32",
donut_show_value = TRUE,
donut_value_suffix = "%",
donut_value_digits = 0,
pie_values = prob_vals,
pie_colors = c("#1976D2", "#FFA000", "#C62828"),
layout = "circle",
curvature = 0.25
)
MIT License. See LICENSE.md for details.